eeg based patient monitoring system for mental alertness

EEG Based Patient Monitoring System for

Mental Alertness Using Adaptive Neuro-Fuzzy

Approach

Dilshad Begum Dept of ISE, Ghousia College of Engineering, Ramanagar, Bangalore, India

K. M. Ravikumar, Special Officer, VTU-Regional Office, Mysore, India

James. Mathew and Sanjeev Kubakaddi ITIE Knowledge Solutions, Bangalore, India

Rajeev Yadav Genia Photonics Inc, Laval, Canada

[email protected]

Abstract—Recent electrophysiological studies support

command-specific changes in the electroencephalography

(EEG) that have promoted their intensive application in the

noninvasive brain computer interfaces (BCI). However,

EEG is plagued by a variety of interferences and noises,

thereby demanding better accuracy and stability for its

application in the neuroprosthetic devices. Here we

investigate wavelets and adaptive neuro-fuzzy classification

algorithms to enhance the classification accuracy of

cognitive tasks. Using a standard cognitive EEG dataset, we

demonstrate improved performance in the classification

accuracy with the proposed system.

Index Terms— BCI, EEG, wavelets, adaptive neuro fuzzy

interface system (ANFIS)

I. INTRODUCTION

A patient recovering following a stroke, severe brain

damage or spinal injury may suffer from unresponsive

wakefulness syndrome (or vegetative state) [1]. Many of

these patients remain unresponsive for prolonged period

of times while some with limited cognitive capabilities.

Rehabilitation of such patients is extremely challenging

due to the difficulty in interpreting their cognitive

abilities. The brain-computer interfaces (BCIs) which

establishes a direct link between a human brain and a

computer discovers the potential of computers to enhance

physical and mental abilities; and often refers to the

interaction of human brain with external devices [2]-[4].

The human brain is a signal generator, sending out

millions of signals which can be measured and analyzed.

Modern biomedical research has provided the method for

extracting these brain signals in a convincing way to

distinguish their characteristics from one another. It was

Manuscript received August 21, 2013; accepted February 24, 2014.

soon discovered that these signals could be used to

communicate with an external peripheral device, through

proper machine learning techniques. Now, BCIs are often

directed at assisting, augmenting, or repairing human

cognitive or sensory-motor functions [5], [6].

Every time we think, move, feel or perform an activity,

the neurons in our brain are at work. This work is carried

out by small electric signals that transmit signals from

neuron to neuron. BCI technology monitors this electrical

activity, usually via the method of

electroencephalography (EEG). EEG characteristics are

then detected via special signal processing algorithms,

and the user is able to classify and generate

communication messages. BCI enables physically

disabled persons to perform many activities, thus

improving their quality of life and allowing them to be

independent [2]-[4], [7]-[9]. A general BCI system uses a

set of EEG samples for training and random samples for

testing the trained classifier. However, the classification

accuracy of the system performance is a great challenge.

Identifying the best discriminating features in the EEG

which is plagued by a variety of noise; in itself is a

challenge then selecting a classification technique

capable to distinguish a variety of time-varying patterns

is another challenge. Widespread research and

experiments have been conducted on algorithms to

classify brain signals according to its characteristic

features.

EEG patterns have been exploited widely in prosthetic

control [10]; for example, they have been used to move

the cursor on a computer display [11], to control hands-

free or virtual wheelchairs [12], [13] and to move robotic

limbs [13]-[17]. Different feature extraction techniques,

such as autoregressive coefficients [18]-[22]; component

analyses; spectral, spatial and correlation features[5], [6],

[21], [23]-[26]; transform features (short-time (windowed)

Journal of Medical and Bioengineering Vol. 4, No. 1, February 2015

©2015 Engineering and Technology Publishingdoi: 10.12720/jomb.4.1.59-66

59

Fourier wavelet, and affine transform) [26]; and time-

frequency-ambiguity features [19] have been

experimented on using EEG signals with standard

clustering and classification methods, including: support

vector machines , K-nearest neighbor, linear discriminant

analysis, Bayesian Classifier, Hidden Markov Model,

neural networks , fuzzy classifiers, genetic algorithm, etc

[27]-[36].

Adaptive Neuro-Fuzzy system is used in [18], [31],

[37]-[40] to detect P-300 rhythm in EEG, which are

obtained from visual stimuli followed by a motor

response from the subject. The paper explored wavelet

transform to extract features together with ICA algorithm.

41] proposed an intelligent system for ECG

classification based on ANFIS theory utilizing ICA

feature extraction. ANFIS algorithm is utilized by

37] to distinguish between imaginary left

and right hand motion using wavelet features, in which

the impact of cluster radius and number of fuzzy rules in

determining classification accuracy is investigated.

42] used five ANFIS classifiers to

distinguish between five different mental tasks and

compared the results with different neural network

architecture like MLP and Hierarchical Hybrid Model. A

43] compared the performance of

different neural network algorithms in classifying EEG

for five mental tasks with respect to wavelet transform

44] explored auto regression

features with neural networks for the discrimination of

45], [46] described a system

for on-line controlling the hand movement in a virtual

reality environment using Real-Time Recurrent

Probabilistic Neural Network.

EEG is considered the best suited potential non-

invasive interface, mainly due to its fine temporal

resolution, ease of use, portability and low set-up cost.

The project focuses on extracting characteristic features

of EEG signals to control an external device, say alarm

system. The research investigates EEG preprocessing,

wavelet feature extraction and Neuro- Fuzzy

classification to control an alarm system, alerting about

one’s mental state.

II.

METHOD

A.

Data Collection

In this research, a standard dataset from Purdue

University has been used. EEG Recording was performed

with a bank of Grass 7P511 amplifiers whose bandpass

analogue filters were set at 0.1 to 100 Hz. Signals are

taken using six electrodes in the central, parietal and

occipital regions - C3 C4 P3 P4 O3 O4 with respect to

10-20 electrode system. EOG signal is recorded

separately and the signals are claimed to be free from

artifacts. Data is collected for two cognitive tasks –

mental letter composing and geometric figure rotation. A

single trial recorded consists of 2500 samples at a

sampling frequency 250Hz. Ten trials of each task is

considered for this study.

Figure 1. Algorithm flowchart for the proposed BCI system.

B. Preprocessing and Feature Extraction

Some pre-processing techniques are to be applied to

the raw EEG signals to reduce error as well as

computational complexity. While recording, EOG and

line frequency artifacts are already removed in the dataset

[47].

Normalization: All the signals have to be

normalized to the range of [-1 1] to make the

calculation simpler.

Filtering: The signals are then fed to an equiripple

bandpass filter set to the range 0-40 Hz [6]. We

are interested only in delta, theta, alpha, beta and

lower gamma band signals, which have actionable

information.

Wavelet feature extraction: Wavelet features are

widely accepted and utilized due to high

discriminating properties in the active band (delta,

theta, alpha, beta, and gamma) for a particular

EEG condition. Wavelet decomposition gives

coefficients in these band areas, which clearly

signifies the brain signal. We considered wavelet

decomposed band coefficients to represent each

signal and to reduce the dimension of feature

matrix statistical parameters that has been selected.

Wavelets divide the signal into different frequency

components in a multi-resolution manner. As the EEG

signals are non-stationary, Fourier Transform may not be

a good choice. Since we couldn’t fix a permanent

window size for EEG patterns a time-frequency

representation is adopted to distinguish the subsequent

frequency components [48].

In wavelet transform, the signal is fed to filter bank

structure, a series of low pass and high pass filters. To

analyze the signal at different scales, different filter cutoff

frequencies are chosen for the sub filters. The resolution

of the signal, which is a measure of the amount of detail

information in the signal, is changed by the filtering

operations, and the scale is changed by up sampling and

down sampling (subsampling) operations.


©2015 Engineering and Technology Publishing

Darvishi et al [

Barbosa et al [

Nazmy et al [

study by Khare et al [

features. Charles et al [

mental tasks. Ahamadi et al [

60

Figure 2. Three level decomposition tree of DWT [49].

The signal can be represented using a Mother wavelet -

( )x and its child wavelets, which are scaled and time

shifted versions of the mother wavelet [48].

2( , ) ( ) 2 2

s

s

s l x x l

(1)

The mother function ( )x is modified with respect to

variables s and l to generate a series of wavelet family.

The scale index s indicates the wavelet's width, and the

location index l gives its position. The mother functions

are rescaled, or dilated by powers of two, and translated

by integers. Thus, the mother wavelet basis functions

give a clear picture of the signal characteristics.

The wavelet transform is the convolution between the

two functions x and , which can be done in Fourier

space using the Fast Fourier Transform (FFT) [48]. In the

Fourier domain, the wavelet transform is simply,

k

1i n t

k

0

( ) ( ) *(s )eN

n

k

W s x k

(2)

where the ^ indicates the Fourier transform (FT), and the

Fourier transform of the time series is given by:

12 /

0

1 Nikn N

k n

n

x x eN

(3)

To use this formula, the FT of the wavelet function

should be known analytically. The wavelets are

normalized as,

1

22( ) ( )k o k

ss s

t

(4)

After preprocessing, the signals are subjected to

wavelet decomposition and the coefficient features are

extracted. The signal is decomposed to 6th level to

extract the bands of interest using Daubechies wavelets

(db14). Since the coefficient matrix is very large, we

apply some statistical data reduction techniques. The final

feature set will be a set of minimum of coefficients,

maximum of coefficients, standard deviation, mean and

power of the coefficient matrix (for each band-alpha, beta,

gamma, delta , theta). Thus for a single electrode signal, a

set of 25 features are extracted. Features are extracted for

8 trials each of two tasks- geometric figure rotation and

letter composing.

C. Classification

The algorithm is trained for a set of data (EEG signals)

which is been already taken from different subjects for

two particular tasks. Since the task is clear identification

and grouping of signal pattern, classification algorithms

are preferred to regression methods. Out of the different

classification algorithms like LDA, KNN, SVM, LVQ,

NN, Neuro-Fuzzy etc, we select classifiers with higher

adaptability and precision; i.e. ANFIS.

1) Neural fuzzy system

The term Neuro-Fuzzy refers to combinations of

artificial neural network and fuzzy logic (Fig 3). Neural

fuzzy systems provide a kind of automatic tuning method

to fuzzy systems by the use of neural networks, but

without altering their functionality. Neural networks can

be used for the membership function elicitation and

mapping between fuzzy sets that are utilized as fuzzy

rules.

Figure 3. Neuro-fuzzy system [50].

During the training process, a neural network adjusts

its weights in order to minimize the mean square error

between the output of the network and the desired output.

In Neuro-Fuzzy systems, the weights of the neural

network represent the parameters of the fuzzification

function, fuzzy word membership function, fuzzy rule

confidences and defuzzification function respectively

[50]. Thus the training of neural network results in

automatically adjusting the parameters of a fuzzy system

and finding their optimal values.

2) Adaptive neuro-fuzzy inference system (ANFIS)

Adaptive Neuro-Fuzzy Inference Systems are a class

of adaptive networks that are functionally equivalent to

fuzzy inference systems, incorporating the learning

capabilities of neural networks and the robustness of

fuzzy.

Fuzzy interface system maps input characteristics to

input membership functions and again this to a set of

rules. Rules could be set by the user’s interpretation of

the model characteristics. The rules are mapped to output

membership functions and then to an output value.

Neuro-adaptive learning techniques provide a method for

the fuzzy modelling procedure to learn about a data set. It

calculates the best membership function to map the given

data using neural network structure. The membership

function parameters changes throughout the learning

process, according to a gradient vector, which describes

the performance index of the fuzzy modelling [51].

Sugeno-Tsukamoto fuzzy model is a widely accepted

neuro-fuzzy system. It is a universal approximator. The

neuro-fuzzy system in this research follows a hybrid

learning algorithm, Least Mean Square Algorithm for

forward pass and Scaled Conjugate Gradient Method for

backward pass. For a fuzzy inference system with two

inputs x and y and one output z, a first-order Sugeno

fuzzy model has rules as the following:


©2015 Engineering and Technology Publishing 61

Input nodes (Layer 1): All nodes are adaptive nodes.

The outputs are fuzzy membership grade of the inputs,

given by

1 ( ), 1,2i iO A x for i (5)

1

2( ), 3,4i iO B y for i (6)

where x and y are the inputs to the node I, A is a

linguistic label (small, large) and ( )iA x and

2 (y)iB can adopt any fuzzy membership function.

For a Gaussian (bell)-shaped function,

2

1( )

1 ( / )iA x

x ci ai b

(7)

where (ai, bi and ci) are the parameters of the

membership function. Parameters are referred to as

premise parameters.

Rule nodes (Layer 2): Rule layer is a fixed node

labelled M whose output is the product of all the

incoming signals. The outputs of this layer are the firing

strengths of the rule, represented as:

2 ( ) (y), i 1,2i i i iO w A x B (8)

Average nodes (Layer 3): In normalization layer also

nodes are fixed nodes, labelled N, which gives

normalized firing strengths

3

1 2/ (w w ), i 1,2i i iO w w (9)

Consequent nodes (Layer 4): It is defuzzification layer

in which all nodes are adaptive nodes. The output of each

node is simply the product of the normalized firing

strength and a first order polynomial.

4

i i i(p x q x r ), i 1,2i i i iO w f w (10)

Output nodes (Layer 5): This is summation neuron, a

fixed node which computes the overall output as the

summation of all incoming signals.

5

1 21/ ( )i i i i ii

O w f w f w w

(11)

3) Scaled conjugate gradient method (SCG)

SCG is a supervised learning algorithm for

feedforward neural networks, and is a member of the

class of conjugate gradient methods.

Figure 4. ANFIS structure [7].

Let expE

pT

be a vector from the space

.degT T , where N is the sum of the number of

weights and of the number of biases of the network. Let

be E be the error function we want to minimize [52].

Contrary to other conjugate gradient methods (CGMs),

SCG differs in two points:

Each iteration k of a CGM computes iw , where

NR is a new conjugate direction, and

1 .k k k kw w p is the size of the step in this

direction. Actually kp is a function of k , the

Hessian matrix of the error function, namely the

matrix of the second derivatives. In contrast to

other CGMs which avoid the complex

computation of the Hessian and approximate

k with a time-consuming line search procedure,

SCG makes the following simple approximation

of the term "( )kE w , a key component of the

computation of :k ks .

As the Hessian is not always positive definite,

which prevents the algorithm from achieving good

performance, SCG uses a scalar k which is

supposed to regulate the indefiniteness of the

Hessian. This is a kind of Levenberg-Marquardt

method, and is done by setting:

"( ).

'( . ) '( ),0 1

k k k k k

k k k kk

k

s E w p p

E w p E w

(12)

and adjusting k at each iteration. This is the main

contribution of SCG to both fields of neural learning and

optimization theory.SCG has been shown to be

considerably faster than standard back propagation and

than other CGMs [52].

The research adopts Neuro-Fuzzy classifier for

classifying the feature matrix into two groups. In this we

use the k-means algorithm to initialize the fuzzy rules.

For that reason, we should give the number of clusters for

each class. Here all the input features are grouped to one

cluster for each class. A Gaussian membership function is

used for fuzzy set descriptions, because of its simple

derivative expressions

The Scale gradient classifier based is on Jang’s neuro-

fuzzy classifier [51]. The differences are about the rule

weights and parameter optimization. The rule weights are

adapted by the number of rule samples. The scaled

conjugate gradient (SCG) algorithm is used to determine

the optimum values of nonlinear parameters. The SCG is

faster than the steepest descent and some second order

derivative based methods. Also, it is suitable for large

scale problems [53], [54].

4) K-means clustering:



The K-means algorithm divides the data into k

mutually exclusive clusters with actual observations. The

clusters are defined by its member objects and centroid.

The sum of distances from all objects in that cluster is

minimized at the centroid. The objects are moved towards

cluster centres using distance measurements like

Euclidean, Mahalanobis, city block etc. and is iterated till

the set of clusters become well- separable.

D. Testing Phase

Once the training is done, a fis structure is created with

two classes EEG signals of random task are given to the

system for testing The signals are pre-processed and

wavelet features are extracted. This is tested with the fis

structure and the neuro-fuzzy system returns the degree

of closeness to either of the classes. This degree of

closeness is rounded and is used to generate activation

signals to control external device. If the test signal is

grouped to any thought process, the alarm is set and alerts

about the mental activeness of the subject.

E. External Device Control

The objective of the project is to control an alarm and

led with respect to the classifier output. We use the

Arduino UNO board to set up the external device.

The Arduino UNO board with ATmega328

microcontroller is used to facilitate external device

control. In the board pin6 is connected to LED and pins 7,

9 and 11 are connected to 8ohm speakers. A simple

program is loaded to the controller to activate the output

pins when a digital 1 is coming through the serial port.

Speaker ports are programmed with suitable delay to

sound the output as an alarm. The system is tested

successfully for random test signals.

TABLE I. ACTIVATION SIGNAL FOR OUTPUT CONTROL

Index FIS Structure Activation Signal Device Control

1 Letter composing 0 No action

2 Figure Rotation 1 LED is ON

Alarm is ON

III. RESULTS AND DISCUSSION

The project investigates the relation between EEG

features and Fuzzy rules connecting them. Four different

clustering techniques- K means, grid partitioning,

subtractive clustering and Fuzzy C means are

experimented with respect to two backpropogation

network training algorithms, Levenberg-Marquardt and

Scaled gradient conjugate.

Figure 5. Training error for the SGC.

Figure 6. SGC membership function before and after training.

Figure 7. Fuzzy rules for the SGC network



(a): Grid Partitioning (b): FCM Clustering (c): Subtractive Clustering (d): Proposed SGC- k-means Clustering

Figure 8. Compares the membership Functions, Surface mesh and the network models of LM algorithm with (a) Grid Partitioning (b) Fuzzy C means (c) Subtractive Clustering(d) Proposed SGC algorithm with K means for EEG pattern classification.

The system is trained for 2 tasks over 500 iterations.

1) SCG-K means clustering:

For SGC algorithm, the training and testing

recognition rate got converged to 100% and 92.5%, the

mean square error got reduced to 0.0353 as shown in Fig.

5.

The membership functions that map the input to the

rule base are user defined Gaussian functions and got

reconfigured with the neural training epochs as shown in

Fig 6. Initially the membership function is randomly

defined, after setting the rules and fuzzification, the

membership function got reshaped to accommodate range

all range of feature values through the selected input

nodes. For each input set the membership function

defines two clusters for two classes, utilizing K-mean

clustering technique.

Fuzzy sets AND rules for all the input clusters and

classifies into two groups. These rules are put into the

network as weights and after every training loop, the

input functions got adjusted since the fuzzy weights are

set to be strict and not subjected to any variations. The

fuzzification process is detailed in Fig. 7.

2) Levenberg Marquardt- grid partitioning, fuzzy C

means, subtractive clustering:

The Levenberg-Marquardt algorithm approaches the

second-order training speed without calculating Hessian

matrix. The Levenberg-Marquardt algorithm

approximates Hessian matrix in the following Newton-

like update:

1

1 [ ]T T

k kX X J J I J e

(13)

where J is the Jacobian matrix that contains first

derivatives of the network errors with respect to the

weights and biases, and e is a vector of network errors.

Using a standard backpropagation technique Jacobian

matrix can be calculated, that is much simpler compared

to computing the Hessian matrix. When μ=0, this is

Newton’s method, for large μ, this becomes a gradient

descent with small step size. After each step μ is

decreased to reach near error minimum and is increased

only when a step would increase the performance

function. Thus the performance function got reduced in

each iteration step.

In subtractive clustering, each data point is assumed to

be a potential cluster center and a measure of the

likelihood that each data point is calculated based on the

density of surrounding data points. The data point with

the highest potential is selected to be the first cluster

center. Then all data points in the vicinity of the first

cluster center (as determined by radii) are removed so as

to determine the next data cluster and its center location.

This process is iterated until all of the data is within radii

of a cluster center [24].

In FCM each data point is grouped to a cluster to some

degree which is specified by a membership grade. The

algorithm starts with an initial guess for the cluster

centers, which is most likely incorrect. The cluster

centers and the membership grades for each data point are

updated iteratively till the cluster centers to the right

location within a data set. This iteration is subjected to

minimize an objective function which provides the

distance from any given data point to a cluster center that

is weighted by the membership grade of that data point.

TABLE II. COMPARISON OF TRAINING ERROR

LM-

Grid

LM-

Subtractive

LM-FCM SGC-K

means

Error

(500 iterations)

0.3383 0.4540 0.4525 0.0353

From Fig. 8, we can conclude that for FCM and

subtractive clustering, the network model is less complex

than grid partitioning. K means clusters follow straight

forward approach in getting the classifier output to either

of the two groups. The membership functions got

adjusted to the fuzzy rules during training and FCM and

subtractive methods form clusters for each set of input

features. While in K means, all the input features got

clustered into two classes, regardless the number of

features provided and the user defined Gaussian

membership functions got adjusted to accommodate the

varying data points. The surface mesh energy output is

more optimized in SGC method. The network gets

overloaded when the input features are separately fed to

the LM algorithm and it takes a long time to train the

fuzzy network, but SGM is observed to avoid that time

lag as it approaches the minimum error point through a



faster and optimum path. The training error for 500

iterations got compared in Table II, and SGC approach

gives a comparatively low error rate.

After the classification system is tested with random

signals and the degree of closeness to specific classes are

measured. This degree of closeness is used to group the

signal into letter composing and figure rotation. For

letter composing the system remain in the same state but

for figure rotation an alarm signal is evoked.

IV.

In this paper, wavelet based feature extraction is

incorporated with Adaptive Neuro- Fuzzy Interface

System classifier and various clustering and training

algorithms got compared. The Neuro-Fuzzy classifier

provides the information about the link between input

features and the relationship with corresponding classes,

adopting data clustering logic to form well-separable

groups. The SGM and LM algorithms optimize the

network to reduce the classification error, and it is seen

that the SGM gives better classification results. The rule

based classifier is proved capable enough to distinguish

different mental activities which in turn activate the

external device. The project proves the possibility of

controlling external devices using mental thoughts in an

optimized way via Neuro –Fuzzy EEG processing.

ACKNOWLEDGMENT

The Authors would like to thank the University of

Purdue, USA, for providing the EEG data and ITIE Inc

for providing the necessary equipment’s to carry out the

experiments.

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Dilshad Begum is a PhD candidate in Electrical and Electronics

Research Center Ghousia College of Engineering, Ramanagara Karnataka, under Visvesvaraya Technological University (VTU), India.

She received BE Degree in Computer Science and Engineering from

Gulbarga University Gulbarga in 2000 and MTech Degree from Visvesvaraya Technological University (VTU) in 2007 .Her current

area of interest is in EEG data Processing, Brain Computer Interface and

its Applications. ([email protected])

Ravi Kumar K. M Professor and Head of Information Science and Engineering Department, Ghousia collage of Engineering,

Ramanagaram. He has completed his M.Tech in the field of Biomedical

Instrumentation in the year 2002 and submitted his PhD thesis in 2009 (VTU, Belgaum). He is in the field of teaching from past 15 years and

he has published ten papers in the International Conference and four in

the International journal related to his research areas. He is member of various professional societies which include ISTE, MIE, SIS, and

BMESI. His field of interest includes Digital Signal Processing,

Human Computer Interface and Communication Systems. He is serving as Special Officer VTU, Belgaum ([email protected]).

Sanjeev Kubakaddi is successful entrepreneur and proprietor of itie Knowledge Solutions Bangalore, company focusing on Biomedical,

Embedded product development and technology training. He received

his Bachelors in 1995, Master of Technology (M. Tech) in 2012. He is also working towards his PhD from Jain University. He is a renowned

trainer in the areas of DSP, Image Processing and expert in Matlab®,

Simulink® and Texas Instruments DSP. Mr. Sanjeev Kubakaddi ([email protected]) is successful entrepreneur and proprietor of itie

Knowledge Solutions Bangalore, company focusing on Biomedical,

Embedded product development and technology training. He received his Bachelors in 1995, Master of Technology (M. Tech) in 2012. He is

also working towards his PhD from Jain University. He is a renowned

trainer in the areas of DSP, Image Processing and expert in Matlab®, Simulink® and Texas Instruments DSP ([email protected]).

James Mathew completed his Bachelors in Electrical & Electronics

Engineering from MG University, Kerala, India and Masters in

Communication Engineering & Signal Processing from University of Plymouth, UK. His research interests are Cognitive Neuroscience,

Neural Engineering, Brain Computer Interface, Image Processing,

Machine Learning and Pattern Recognition.

Rajeev Yadav received the B.Sc. degree in physics and the M.Sc. degree in electronics both from the D.D.U. Gorakhpur University,

Gorakhpur, India, in 1998 and 2000, respectively and Ph.D. in electrical

engineering from the Concordia University, Montreal, Canada in 2012. He is currently a Research Scientist at the Genia Photonics Inc., Laval,

Canada and a recipient of the prestigious NSERC Industrial R&D

fellowship of Canada. Prior to this, he worked as a Research Scientist at the Leap Medical Inc. Montreal, Canada. He is member of the IEEE

EMB, SP, CAS societies, CMBES, OSA and IBRO. He serves as

committee member for the CMBES and OSA, editorial member and reviewer for over 30 international signal processing and biomedical

engineering journals. His research interests include biophotonics,

biomedical signal processing and pattern recognition with emphasis on their application to neural signals for critical care and neuroscience

research, and neural prosthesis.



eeg based patient monitoring system for mental alertness

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